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Strong employer brand, metro location, generalist Data Engineer title, and broad required skills increase competition.
Low — core data engineering skills like ETL, Spark, and data warehouses transfer easily across industries.
Moderate due to many required data technologies (Kafka, Spark, data lakes) but no explicit years requirement.
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Design, develop, and implement scalable data pipelines and automated validation/testing frameworks for large datasets.
Manage and optimize data storage solutions including data warehouses, data lakes, and cloud-based systems ensuring scalability, reliability, and security.
Research and integrate new data engineering tools and methodologies while collaborating cross-functionally to deliver high-quality, scalable engineering solutions aligned with business goals.
Proficiency in data integration (ETL), data analysis, programming languages (Python, R, SQL), and data modeling.
Foundational knowledge of big data processing frameworks such as Apache Hadoop and Apache Spark.
Experience with data storage solutions including data warehouses, data lakes, and cloud storage technologies.
Work Experience Required: Not explicitly mentioned in the JD.
Demonstrates effective application of core data engineering skills independently with minimal support, indicating proficiency level.
Experienced in handling large-scale data architectures and optimizing storage and pipelines for scalability and performance.
Capable of researching and integrating new technologies and collaborating across teams to align engineering solutions with business needs.